Integrating Machine Learning and Multi-omics to Identify Key SUMOylation Molecular Signature in Sarcoma

BackgroundBreast cancer (BC) is a highly malignant and heterogeneous tumor which is currently the cancer with the highest incidence and seriously endangers the survival and prognosis of patients. Aging, as a research hotspot in recent years, is widely considered to be involved in the occurrence and development of a variety of tumors. However, the relationship between aging-related genes (ARGs) and BC has not yet been fully elucidated.Materials and methodsThe expression profiles and clinicopathological data were acquired in the Cancer Genome Atlas (TCGA) and the gene expression omnibus (GEO) database. Firstly, the differentially expressed ARGs in BC and normal breast tissues were investigated. Based on these differential genes, a risk model was constructed composed of 11 ARGs via univariate and multivariate Cox analysis. Subsequently, survival analysis, independent prognostic analysis, time-dependent receiver operating characteristic (ROC) analysis and nomogram were performed to assess its ability to sensitively and specifically predict the survival and prognosis of patients, which was also verified in the validation set. In addition, functional enrichment analysis and immune infiltration analysis were applied to reveal the relationship between the risk scores and tumor immune microenvironment, immune status and immunotherapy. Finally, multiple datasets and real‐time polymerase chain reaction (RT-PCR) were utilized to verify the expression level of the key genes.ResultsAn 11-gene signature (including FABP7, IGHD, SPIB, CTSW, IGKC, SEZ6, S100B, CXCL1, IGLV6-57, CPLX2 and CCL19) was established to predict the survival of BC patients, which was validated by the GEO cohort. Based on the risk model, the BC patients were divided into high- and low-risk groups, and the high-risk patients showed worse survival. Stepwise ROC analysis and Cox analyses demonstrated the good performance and independence of the model. Moreover, a nomogram combined with the risk score and clinical parameters was built for prognostic prediction. Functional enrichment analysis revealed the robust relationship between the risk model with immune-related functions and pathways. Subsequent immune microenvironment analysis, immunotherapy, etc., indicated that the immune status of patients in the high-risk group decreased, and the anti-tumor immune function was impaired, which was significantly different with those in the low-risk group. Eventually, the expression level of FABP7, IGHD, SPIB, CTSW, IGKC, SEZ6, S100B, CXCL1, IGLV6-57 and CCL19 was identified as down-regulated in tumor cell line, while CPLX2 up-regulated, which was mostly similar with the results in TCGA and Human Protein Atlas (HPA) via RT-PCR.ConclusionsIn summary, our study constructed a risk model composed of ARGs, which could be used as a solid model for predicting the survival and prognosis of BC patients. Moreover, this model also played an important role in tumor immunity, providing a new direction for patient immune status assessment and immunotherapy selection.

Ubiquitination is one of the most prevalent and complex post-translational modifications of proteins in eukaryotes, playing a critical role in regulating various physiological and pathological processes. Targeting ubiquitination pathways, either through inhibition or activation, holds promise as a novel therapeutic approach for cancer treatment. However, the expression patterns, prognostic significance, and underlying mechanisms of ubiquitination-related genes (URGs) in sarcoma (SARC) remain unclear. We analyzed URG expression patterns and prognostic implications in TCGA-SARC using public databases, identifying DEGs related to ubiquitination among SARC molecular subtypes. Functional enrichment analysis elucidated their biological significance. Prognostic signatures were developed using LASSO-Cox regression, and a predictive nomogram was constructed. External validation was performed using GEO datasets and clinical tissue samples. The association between URG risk scores and various clinical parameters, immune response, drug sensitivity, and RNA modification regulators was investigated. Integration of data from multiple sources and RT-qPCR confirmed upregulated expression of prognostic URGs in SARC. Single-cell RNA sequencing data analyzed URG distribution across immune cell types. Prediction analysis identified potential target genes of microRNAs and long non-coding RNAs. We identified five valuable genes (CALR, CASP3, BCL10, PSMD7, PSMD10) and constructed a prognostic model, simultaneously identifying two URG-related subtypes in SARC. The UEGs between subtypes in SARC are mainly enriched in pathways such as Cell cycle, focal adhesion, and ECM-receptor interaction. Analysis of URG risk scores reveals that patients with a low-risk score have better prognoses compared to those with high-risk scores. There is a significant correlation between DRG riskscore and clinical features, immune therapy response, drug sensitivity, and genes related to pan-RNA epigenetic modifications. High-risk SARC patients were identified as potential beneficiaries of immune checkpoint inhibitor therapy. We established regulatory axes in SARC, including CALR/hsa-miR-29c-3p/LINC00943, CASP3/hsa-miR-143-3p/LINC00944, and MIR503HG. RT-qPCR data further confirmed the upregulation of prognostic URGs in SARC. Finally, we validated the prognostic model's excellent predictive performance in predicting outcomes for SARC patients. We discovered a significant correlation between aberrant expression of URGs and prognosis in SARC patients, identifying a prognostic model related to ubiquitination. This model provides a basis for individualized treatment and immunotherapy decisions for SARC patients.

Disulfidptosis, a newly discovered type of programmed cell death, could be a mechanism of cell death controlled by SLC7A11. This could be closely associated with tumor development and advancement. Nevertheless, the biological mechanism behind disulfidptosis-related genes (DRGs) in sarcoma (SARC) is uncertain. This study identified three valuable genes (SLC7A11, RPN1, GYS1) associated with disulfidptosis in sarcoma (SARC) and developed a prognostic model. The multiple databases and RT-qPCR data confirmed the upregulated expression of prognostic DRGs in SARC. The TCGA internal and ICGC external validation cohorts were utilized to validate the predictive model capacity. Our analysis of DRG riskscores revealed that the low-risk group exhibited a more favorable prognosis than the high-risk group. Furthermore, we observed a significant association between DRG riskscores and different clinical features, immune cell infiltration, immune therapeutic sensitivity, drug sensitivity, and RNA modification regulators. In addition, two external independent immunetherapy datasets and clinical tissue samples were collected, validating the value of the DRGs risk model in predicting immunotherapy response. Finally, the SLC7A11/hsa-miR-29c-3p/LINC00511, and RPN1/hsa-miR-143-3p/LINC00511 regulatory axes were constructed. This study provided DRG riskscore signatures to predict prognosis and response to immunotherapy in SARC, guiding personalized treatment decisions.

Breast cancer (BRCA) continues to pose a serious risk to women's health worldwide. Neoadjuvant chemotherapy (NAC) is a critical treatment strategy. Nevertheless, the heterogeneity in treatment outcomes necessitates the identification of reliable biomarkers and prognostic models. Programmed cell death (PCD) pathways serve as a critical factor in tumor development and treatment response. However, the relationship between the diverse patterns of PCD and NAC in BRCA remains unclear. We integrated machine learning and multiple bioinformatics tools to explore the association between 19 PCD patterns and the prognosis of NAC within a cohort of 921 BRCA patients treated with NAC from seven multicenter cohorts. A prognostic risk model based on PCD-related genes (PRGs) was constructed and evaluated using a combination of 117 machine learning algorithms. Immune infiltration analysis, mutation analysis, pharmacological analysis, and single-cell RNA sequencing (scRNA-seq) were conducted to explore the genomic profile and clinical significance of these model genes in BRCA. Immunohistochemistry (IHC) was employed to validate the expression of select model genes (UGCG, BTG22, TNFRSF21, and MYB) in BRCA tissues. We constructed a PRGs prognostic risk model by using a signature comprising 20 PCD-related DEGs to forecast the clinical outcomes of NAC in BRCA patients. The prognostic model demonstrated excellent predictive accuracy, with a high concordance index (C-index) of 0.772, and was validated across multiple independent datasets. Our results demonstrated a strong association between the developed model and the survival prognosis, clinical pathological features, immune infiltration, tumor microenvironment (TME), gene mutations, and drug sensitivity of NAC for BRCA patients. Moreover, IHC studies further demonstrated that the expression of certain model genes in BRCA tissues was significantly associated with the efficacy of NAC and emerged as an autonomous predictor of outcomes influencing the outcome of patients. We are the first to integrate machine learning and bulk and scRNA-seq to decode various cell death mechanisms for the prognosis of NAC in BRCA. The developed unique prognostic model, based on PRGs, provides a novel and comprehensive strategy for predicting the NAC outcomes of BRCA patients. This model not only aids in understanding the mechanisms underlying NAC efficacy but also offers insights into personalized treatment strategies, potentially improving patient outcomes.

Breast cancer is the most common type of cancer in women, and while current treatments can cure the majority of early-stage primary BC cases, recurrence remains a significant challenge. Traditional methods of assessing patient prognosis, such as AJCC, TNM staging, and biochemical markers, are no longer sufficient in the era of precision medicine. Existing tumor models often rely on single selection and simpler algorithms, which can lead to poor effectiveness or overfitting. To address these limitations, this study systematically analyzed RNA-seq high-throughput data and combined 10 machine learning algorithms to construct 117 models. The optimal algorithm combination, StepCox[both] and ridge regression, was identified, and an immune-related gene signature (IRGS) composed of 12 genes was developed. The IRGS demonstrated outstanding predictive performance across multiple datasets and surpassed 10 previously published signatures. GSEA analysis revealed significant enrichment differences in cellular processes, diseases, and immune-related pathways between high- and low-risk recurrence patients. The low recurrence risk group based on IRGS exhibited a stronger immune phenotype and better survival prognosis, which may be associated with higher infiltration of CD4 + and CD8 + T cells. However, high M2 macrophage infiltration suggests potential immune escape in low recurrence risk patients. Combined with immune checkpoint expression levels and TIDE results, it is suggested that low-risk patients may respond positively to immunotherapy. Through drug sensitivity analysis, potential drugs that are more effective for both high- and low-risk groups have been identified. Therefore, the IRGS developed in this study can serve as an adjunct tool for assessing the recurrence risk of breast cancer, potentially enhancing personalized treatment planning, and improving the clinical management of patients with breast cancer.

AimImmune cells that infiltrate the tumor microenvironment (TME) are associated with cancer prognosis. The aim of the current study was to identify TME related gene signatures related to the prognosis of sarcoma (SARC) by using the data from The Cancer Genome Atlas (TCGA).MethodsImmune and stromal scores were calculated by estimation of stromal and immune cells in malignant tumor tissues using expression data algorithms. The least absolute shrinkage and selection operator (lasso) based cox model was then used to select hub survival genes. A risk score model and nomogram were used to predict the overall survival of patients with SARC.ResultsWe selected 255 patients with SARC for our analysis. The Kaplan–Meier method found that higher immune (p = 0.0018) or stromal scores (p = 0.0022) were associated with better prognosis of SARC. The estimated levels of CD4+ (p = 0.0012) and CD8+ T cells (p = 0.017) via the tumor immune estimation resource were higher in patients with SARC with better overall survival. We identified 393 upregulated genes and 108 downregulated genes (p < 0.05, fold change >4) intersecting between the immune and stromal scores based on differentially expressed gene (DEG) analysis. The univariate Cox analysis of each intersecting DEG and subsequent lasso-based Cox model identified 11 hub survival genes (MYOC, NNAT, MEDAG, TNFSF14, MYH11, NRXN1, P2RY13, CXCR3, IGLV3-25, IGHV1-46, and IGLV2-8). Then, a hub survival gene-based risk score gene signature was constructed; higher risk scores predicted worse SARC prognosis (p < 0.0001). A nomogram including the risk scores, immune/stromal scores and clinical factors showed a good prediction value for SARC overall survival (C-index = 0.716). Finally, connectivity mapping analysis identified that the histone deacetylase inhibitors trichostatin A and vorinostat might have the potential to reverse the harmful TME for patients with SARC.ConclusionThe current study provided new indications for the association between the TME and SARC. Lists of TME related survival genes and potential therapeutic drugs were identified for SARC.

B-Myb is a critical transcription factor in regulating cell cycle. Dysregulated expression of B-Myb promotes tumor formation and development. B-Myb is a proto-oncogene ubiquitously expressed in proliferating cells, which maintains normal cell cycle progression. It participates in cell apoptosis, tumorigenesis and aging. In addition, B-Myb is overexpressed in several malignant tumors, including breast cancer, lung cancer and hepatocellular carcinoma, and is associated with tumor development. B-Myb expression is also associated with the prognosis of patients with malignant tumors. Both microRNAs and E2F family of transcription factors (E2Fs) contribute to the function of B-Myb. The present review highlights the association between B-Myb and malignant tumors, and offers a theoretical reference for the diagnosis and treatment of malignant tumors.

Dengue fever, caused by the dengue virus (DENV), presents a significant global health concern, with millions of cases reported annually. Despite significant progress in understanding Dengue fever, effective prognosis and treatment remain elusive due to the complex clinical presentations and limitations in current diagnostic methods. The virus, transmitted primarily by the Aedes aegypti mosquito, exists in four closely related forms, each capable of causing flu-like symptoms ranging from mild febrile illness to severe manifestations such as plasma leakage and hemorrhagic fever. Although advancements in diagnostic techniques have been made, early detection of severe dengue remains difficult due to the complexity of its clinical presentations. This study conducted a comprehensive analysis of differential gene expression in dengue fever patients using multiple microarray datasets from the NCBI GEO database. Through bioinformatics approaches, 163 potential biomarker genes were identified, with some overlapping previously reported biomarkers and others representing novel candidates. Notably, AURKA, BUB1, BUB1B, BUB3, CCNA2, CCNB2, CDC6, CDK1, CENPE, EXO1, NEK2, ZWINT, and STAT1 were among the most significant biomarkers. These genes are involved in critical cellular processes, such as cell cycle regulation and mitotic checkpoint control, which are essential for immune cell function and response. Functional enrichment analysis revealed that the dysregulated genes were predominantly associated with immune response to the virus, cell division, and RNA processing. Key regulatory genes such as AURKA, BUB1, BUB3, and CDK1 are found to be involved in cell cycle regulation and have roles in immune-related pathways, underscoring their importance in the host immune response to Dengue virus infection. This study provides novel insights into the molecular mechanisms underlying Dengue fever pathogenesis, highlighting key regulatory genes such as AURKA and CDK1 that could serve as potential biomarkers for early diagnosis and targets for therapeutic intervention, paving the way for improved management of the disease.

Targeting the Synthetic Lethality Interaction of MTAP and PRMT5 to Overcome Drug Resistance and Enhance Anti-Cancer Immunity in Mantle Cell Lymphoma

BackgroundRBM10ʹs function in hepatocellular carcinoma (HCC) has rarely been addressed. We intend to explore the prognostic significance and therapeutic meaning of RBM10 in HCC in this study.MethodsMultiple common databases were integrated to analyze the expression status and prognostic meaning of RBM10 in HCC. The relationship between RBM10 mRNA level and clinical features was also assessed. Multiple enrichment analyses of the differentially expressed genes between RBM10 high- and low- transcription groups were constructed by using R software (version 4.0.2). A Search Tool for Retrieval of Interacting Genes database was used to construct the protein–protein interaction network between RBM10 and other proteins. A tumor immune estimation resource database was employed to identify the relationship between RBM10 expression and immune cell infiltrates. The prognostic value of RBM10 expression was validated in our HCC cohort by immunohistochemistry test.ResultsThe transcription of RBM10 mRNA was positively correlated with tumor histologic grade (p < 0.001), T classification (p < 0.001), and tumor stage (p < 0.001). High transcription of RBM10 in HCC predicted a dismal overall survival (p = 0.0037) and recurrence-free survival (p < 0.001). Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and Gene Set Enrichment Analysis all revealed that RBM10 was involved in the regulation of cell cycle, DNA replication, and immune-related pathways. Tumor immune estimation analysis revealed that RBM10 transcription was positively related to multiple immune cell infiltrates and the expressions of PD-1 and PD-L1.ConclusionRBM10 was demonstrated to be a dismal prognostic factor and a potential biomarker for immune therapy in HCC in that it may be involved in the immune-related signaling pathways.

Hereditary and environmental epidemiology of sarcomas

Tissue resident memory T cells (TRM) are a specialized subset of long-lived memory T cells that reside in peripheral tissues. However, whether TRM exerts any immunosurveillance role in the tumor immune microenvironment (TIME) and progression of non-small-cell lung cancer (NSCLC), which accounts for 85% of all lung cancers, remains unclear. Our comprehensive analysis of multiple independent single-cell and bulk RNA-seq datasets of patient NSCLC samples generated reliable, unique TRM signatures, through which we could infer the abundance of TRM in NSCLC. We discovered that TRM abundance is consistently positively correlated with CD4+ T helper 1 cells, M1 macrophages, and resting dendritic cells in TIME and significantly impacts the prognosis of NSCLC patients. In addition, TRM signatures are strongly associated with immune checkpoint genes and the prognosis of NSCLC patients, suggesting that TRM signatures are promising prognostic markers for immunotherapy in NSCLC. We then built a machine learning model to predict patient survival based on the TRM signatures and immune related genes. The accuracy of the model was validated by Kaplan-Meyer survival analysis, receiver operating characteristic curves, principal component analysis, and t-distributed random neighbor embedding. We developed a 4-gene risk score that effectively stratified patients into low-risk and high-risk categories. The patients with high-risk scores had significantly lower overall survival than patients with low-risk. The prognostic value of the risk score was independently validated by the Cancer Genome Atlas Program (TCGA) dataset and multiple independent NSCLC patient datasets. Notably, low-risk NSCLC patients with higher TRM infiltration exhibited enhanced T-cell activation, macrophage regulation, and other TIME immune responses related pathways, indicating a more active immune profile benefitting from immunotherapy. Altogether, this study provides valuable insights into the complex interactions between NSCLC TRM and TIME and their impact on patient prognosis, highlighting the importance of TRM in shaping the NSCLC microenvironment. The development of a simplified 4-gene risk score provides a practical prognostic marker for risk stratification. Keywords: Tissue resident memory T cell, non-small-cell lung cancer, prognosis, tumor immune microenvironment, machine learning Citation Format: Aidan Shen, Aliesha Garrett, Junhua Mai, Yangzhi Zhu, Chongming Jiang. Tissue resident memory T cell abundances impact non-small-cell lung cancer immune microenvironment and patient prognosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7323.

SNAI family members are transcriptional repressors that induce epithelial-mesenchymal transition during biological development. SNAIs both have tumor-promoting and tumor-inhibiting effect. There are key regulatory effects on tumor onset and development, and patient prognosis in infiltrations of immune cell and tumor microenvironmental changes. However, the relationships between SNAIs and immune cell infiltration remain unclear. We comprehensively analyzed the roles of SNAIs in cancer. We used Oncomine and TCGA data to analyze pan-cancer SNAI transcript levels. By analyzing UALCAN data, we found correlations between SNAI transcript levels and breast cancer patient characteristics. Kaplan–Meier plotter analysis revealed that SNAI1 and SNAI2 have a bad prognosis, whereas SNAI3 is the opposite. Analysis using the cBio Cancer Genomics Portal revealed alterations in SNAIs in breast cancer subtypes. Gene Ontology analysis and gene set enrichment analysis were used to analyze differentially expressed genes related to SNAI proteins in breast cancer. We used TIMER to analyze the effects of SNAI transcript levels, mutations, methylation levels, and gene copy number in the infiltration of immune cell. Further, we found the relationships between immune cell infiltration, SNAI expression levels, and patient outcomes. To explore how SNAI proteins affect immune cell, we further studied the correlations between immunomodulator expression, chemokine expression, and SNAI expression. The results showed that SNAI protein levels were correlated with the expression of several immunomodulators and chemokines. Through analysis of PharmacoDB data, we identified antitumor drugs related to SNAI family members and analyzed their IC50 effects on various breast cancer cell lines. In summary, our study revealed that SNAI family members regulate different immune cells infiltrations by gene copy number, mutation, methylation, and expression level. SNAI3 and SNIA1/2 have opposite regulatory effects. They all play a key role in tumor development and immune cell infiltration, and can provide a potential target for drug therapy.

To identify potential biomarkers and explore the mechanisms underlying diabetic nephropathy (DN) by integrating machine learning, Mendelian randomization (MR) and experimental validation. Microarray and RNA-sequencing datasets (GSE47184, GSE96804, GSE104948, GSE104954, GSE142025 and GSE175759) were obtained from the Gene Expression Omnibus database. Differential expression analysis identified the differentially expressed genes (DEGs) between patients with DN and controls. Diverse machine learning algorithms, including least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest, were used to enhance gene selection accuracy and predictive power. We integrated summary-level data from genome-wide association studies on DN with expression quantitative trait loci data to identify genes with potential causal relationships to DN. The predictive performance of the biomarker gene was validated using receiver operating characteristic (ROC) curves. Gene set enrichment and correlation analyses were conducted to investigate potential mechanisms. Finally, the biomarker gene was validated using quantitative real-time polymerase chain reaction in clinical samples from patients with DN and controls. Based on identified 314 DEGs, seven characteristic genes with high predictive performance were identified using three integrated machine learning algorithms. MR analysis revealed 219 genes with significant causal effects on DN, ultimately identifying one co-expressed gene, carbonic anhydrase II (CA2), as a key biomarker for DN. The ROC curves demonstrated the excellent predictive performance of CA2, with area under the curve values consistently above 0.878 across all datasets. Additionally, our analysis indicated a significant association between CA2 and infiltrating immune cells in DN, providing potential mechanistic insights. This biomarker was validated using clinical samples, confirming the reliability of our findings in clinical practice. By integrating machine learning, MR and experimental validation, we successfully identified and validated CA2 as a promising biomarker for DN with excellent predictive performance. The biomarker may play a role in the pathogenesis and progression of DN via immune-related pathways. These findings provide important insights into the molecular mechanisms underlying DN and may inform the development of personalized treatment strategies for this disease.

INTRODUCTION:Primary biliary cholangitis (PBC) is a chronic autoimmune liver disease that gradually progresses, making early diagnosis and treatment challenging. Reliable biomarkers could enhance diagnostic accuracy and therapeutic development.METHODS:This study analyzed 3 publicly available gene expression data sets from the Gene Expression Omnibus database: GSE119600 (90 patients with PBC and 47 healthy controls), GSE159676 (12 PBC patients and 6 controls), and GSE61260 (11 patients with PBC and 38 controls). To identify genes closely linked to PBC, we applied machine learning techniques, including Least Absolute Shrinkage and Selection Operator, Support Vector Machine-Recursive Feature Elimination, and random forest. We subsequently conducted gene set enrichment and immune cell infiltration analyses to investigate their biological significance. IN addition, potential drug interactions were explored through the Drug Gene Interaction Database, and a competing endogenous RNA regulatory network was developed to examine gene regulation. Finally, the expression of selected genes was validated through multiplex immunofluorescence staining of liver tissue samples from patients with PBC.RESULTS:We identified proteasome subunit beta 7, TRAF family member associated nuclear factor kappa-light-chain-enhancer of activated B cells activator Albumin (TANK)-binding kinase 1, solute carrier family 29 member 1, and natural killer cell receptor 2B4 as key genes associated with PBC; these genes were significantly enriched in immune-related pathways and strongly correlated with immune regulation. Drug target prediction indicated that some genes could interact with existing immunomodulators or anticancer drugs. Competing endogenous RNA network analysis revealed that TANK-binding kinase 1, solute carrier family 29 member 1, and natural killer cell receptor 2B4 interact with multiple miRNAs and long noncoding RNAs, potentially regulating the immune microenvironment of PBC through noncoding RNA mechanisms. Immunofluorescence staining confirmed that these genes were highly expressed in liver tissues from patients with PBC.DISCUSSION:By integrating machine learning and functional analyses, this study identified 4 genes that may serve as potential biomarkers for PBC. Their involvement in immune regulation suggests possible applications in both diagnosis and therapy. Further studies are necessary to explore their clinical relevance and therapeutic potential.